4 Reasons Why Algorithm Driven Service Planning Saves Time and Money

Taking time to service machinery is necessary for operations, but it can slow down, or even halt, productivity, lower efficiency, and decrease profits if not scheduled optimally. By joining adaptive algorithms with service planning, scheduling maintenance becomes automatic, efficient, and cost-effective. Relying on machine data, algorithms can help companies make the transition from a reactive to a proactive scheduling model and resolve maintenance issues before they disrupt production. Optimised service planning has multiple benefits:

1. Proactively scheduling machine maintenance minimises downtime and improves the reliability of equipment, which ultimately increases productivity
2. Service organisation is optimised, since time spent on maintenance and wait time for spare parts are minimised, while employee safety is improved
3. Switching from routine maintenance to accurately forecasted service planning lowers direct and indirect costs, since asset failure becomes a thing of the past, improving the bottom line
4. Work orders can be prioritised based on accurate measures like future OEE, future asset health, and available capacity with data collected, improving efficiency

How Algorithms Support Work Order Prioritisation

First, data from equipment and its surrounding systems are collected, securely held in the centralised cloud, and analysed. Machine learning algorithms recognise failure patterns to indicate future issues before they hinder production and learn trends from past performance to make predictions more accurate. Taking into consideration current asset health, work orders are prioritised based on future performance. Maintenance that is most urgent is scheduled sooner, preventing a costly collapse in the production line. By forecasting the level of necessity of service issues based on data, maintenance is less likely to interfere with productivity.

Example Use Case: Dutch Railways

Dutch Railways is the main rail service provider of public transport by train within the Netherlands. They transport 1.2 million travelers with 5200 trains on a daily basis, and they are dedicated to providing efficient, sustainable, and smart services to their customers.

Algorithms support this vision by guiding service officers in planning effective work orders based on current conditions like equipment and mechanic availability, location, and capabilities. Paired with operators’ expert knowledge, this application improved data-driven planning decisions, provided better service, and lowered CO2 emissions. This stage is the initial step toward making their service planning completely autonomous, driven by algorithms, which will become smarter and more powerful as they learn patterns as more data is captured.

Data-driven decisions in the age of IoT and Industry 4.0 are becoming the norm, and the capabilities to operate with predictive analytics and smart solutions are becoming expected, especially for companies with large and complex machinery. Flexible service planning allows industry leaders to create and maintain sustainable competitive advantages and adapt to forces in the marketplace. Service planning powered by real-time data comparison and forward-looking algorithms allows predictive maintenance to transition manufacturers into the future, while improving their bottom line.

Widget Brain and our partners can help companies set up the data infrastructure they need to optimise service planning. We provide the platform The Algorithm Factory and the algorithms to make machines smarter and more efficient with plugins easy to introduce to your current systems.

Want to know more about the service planning possibilities for your business and our intelligent algorithms? Contact us today at www.widgetbrain.com/demo/ to stay ahead of the pack.

Originally published at widgetbrain.com on July 19, 2018.